Overview

Dataset statistics

Number of variables18
Number of observations378661
Missing cells3801
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory227.6 MiB
Average record size in memory630.2 B

Variable types

CAT9
NUM7
DATE2

Warnings

name has a high cardinality: 375764 distinct values High cardinality
category has a high cardinality: 159 distinct values High cardinality
deadline has a high cardinality: 3164 distinct values High cardinality
launched has a high cardinality: 378089 distinct values High cardinality
duracion has a high cardinality: 262415 distinct values High cardinality
usd_pledged_real is highly correlated with pledged and 1 other fieldsHigh correlation
pledged is highly correlated with usd_pledged_realHigh correlation
usd pledged is highly correlated with usd_pledged_realHigh correlation
usd_goal_real is highly correlated with goalHigh correlation
goal is highly correlated with usd_goal_realHigh correlation
country is highly correlated with currencyHigh correlation
currency is highly correlated with countryHigh correlation
usd pledged has 3797 (1.0%) missing values Missing
goal is highly skewed (γ1 = 70.79927974) Skewed
pledged is highly skewed (γ1 = 75.1517915) Skewed
backers is highly skewed (γ1 = 86.76300901) Skewed
usd pledged is highly skewed (γ1 = 105.8999201) Skewed
usd_pledged_real is highly skewed (γ1 = 82.18751606) Skewed
usd_goal_real is highly skewed (γ1 = 78.22084847) Skewed
name is uniformly distributed Uniform
launched is uniformly distributed Uniform
duracion is uniformly distributed Uniform
ID has unique values Unique
pledged has 52527 (13.9%) zeros Zeros
backers has 55609 (14.7%) zeros Zeros
usd pledged has 68112 (18.0%) zeros Zeros
usd_pledged_real has 52527 (13.9%) zeros Zeros

Reproduction

Analysis started2020-11-22 13:46:37.712269
Analysis finished2020-11-22 13:47:59.346146
Duration1 minute and 21.63 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

ID
Real number (ℝ≥0)

UNIQUE

Distinct378661
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1074731192
Minimum5971
Maximum2147476221
Zeros0
Zeros (%)0.0%
Memory size2.9 MiB
2020-11-22T06:47:59.977693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5971
5-th percentile108769050
Q1538263516
median1075275634
Q31610148624
95-th percentile2039733043
Maximum2147476221
Range2147470250
Interquartile range (IQR)1071885108

Descriptive statistics

Standard deviation619086204.3
Coefficient of variation (CV)0.5760381842
Kurtosis-1.197961577
Mean1074731192
Median Absolute Deviation (MAD)535932505
Skewness-0.002600127514
Sum4.069587879e+14
Variance3.832677284e+17
MonotocityNot monotonic
2020-11-22T06:48:00.289687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12944691191< 0.1%
 
9259777441< 0.1%
 
9591803841< 0.1%
 
10033565651< 0.1%
 
9263934121< 0.1%
 
8093741031< 0.1%
 
11166273531< 0.1%
 
17100387661< 0.1%
 
11386392611< 0.1%
 
13609414711< 0.1%
 
5620187221< 0.1%
 
4256895091< 0.1%
 
5419279201< 0.1%
 
5774012721< 0.1%
 
18134839431< 0.1%
 
21065875621< 0.1%
 
571437231< 0.1%
 
19424710851< 0.1%
 
13196230861< 0.1%
 
10889343191< 0.1%
 
2175246561< 0.1%
 
19890917611< 0.1%
 
6751604651< 0.1%
 
12330479511< 0.1%
 
14537752461< 0.1%
 
Other values (378636)378636> 99.9%
 
ValueCountFrequency (%) 
59711< 0.1%
 
185201< 0.1%
 
211091< 0.1%
 
213711< 0.1%
 
243801< 0.1%
 
338671< 0.1%
 
390361< 0.1%
 
394091< 0.1%
 
465881< 0.1%
 
481391< 0.1%
 
ValueCountFrequency (%) 
21474762211< 0.1%
 
21474723291< 0.1%
 
21474666491< 0.1%
 
21474601191< 0.1%
 
21474552541< 0.1%
 
21474541041< 0.1%
 
21474482291< 0.1%
 
21474460581< 0.1%
 
21474446191< 0.1%
 
21474372781< 0.1%
 

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct375764
Distinct (%)99.2%
Missing4
Missing (%)< 0.1%
Memory size2.9 MiB
New EP/Music Development
 
41
Canceled (Canceled)
 
13
N/A (Canceled)
 
11
Music Video
 
11
Debut Album
 
10
Other values (375759)
378571 
ValueCountFrequency (%) 
New EP/Music Development41< 0.1%
 
Canceled (Canceled)13< 0.1%
 
N/A (Canceled)11< 0.1%
 
Music Video11< 0.1%
 
Debut Album10< 0.1%
 
New EP / Music Development10< 0.1%
 
Cancelled (Canceled)10< 0.1%
 
Reflections9< 0.1%
 
The Journey9< 0.1%
 
The Other Side8< 0.1%
 
Pizza8< 0.1%
 
A Midsummer Night's Dream8< 0.1%
 
The Awakening8< 0.1%
 
Pasta Salad7< 0.1%
 
Choices7< 0.1%
 
Romeo & Juliet6< 0.1%
 
Guacamole6< 0.1%
 
Karma6< 0.1%
 
Chocolate Chip Cookies6< 0.1%
 
a (Canceled)6< 0.1%
 
Through My Eyes6< 0.1%
 
The Wall6< 0.1%
 
Alone6< 0.1%
 
Home6< 0.1%
 
The Mission6< 0.1%
 
Other values (375739)37842799.9%
 
2020-11-22T06:48:02.768043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique373536 ?
Unique (%)98.6%
2020-11-22T06:48:02.998198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length96
Median length34
Mean length34.83487869
Min length1

Overview of Unicode Properties

Unique unicode characters198
Unique unicode categories19 ?
Unique unicode scripts2 ?
Unique unicode blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
178049013.5%
 
e11321348.6%
 
a7472015.7%
 
o7335235.6%
 
i6680445.1%
 
r6622185.0%
 
n6333524.8%
 
t6258594.7%
 
s4894773.7%
 
l4517543.4%
 
h3088852.3%
 
d3078062.3%
 
u2910232.2%
 
c2813322.1%
 
m2256061.7%
 
g1899001.4%
 
T1849561.4%
 
S1826031.4%
 
A1733961.3%
 
y1716231.3%
 
C1688021.3%
 
p1614961.2%
 
f1349401.0%
 
P1221420.9%
 
b1156040.9%
 
Other values (173)224644417.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter867960365.8%
 
Uppercase Letter215054716.3%
 
Space Separator178054313.5%
 
Other Punctuation3030712.3%
 
Decimal Number970020.7%
 
Dash Punctuation920480.7%
 
Close Punctuation369220.3%
 
Open Punctuation367380.3%
 
Math Symbol82960.1%
 
Other Symbol2564< 0.1%
 
Final Punctuation1177< 0.1%
 
Currency Symbol882< 0.1%
 
Connector Punctuation545< 0.1%
 
Initial Punctuation373< 0.1%
 
Modifier Symbol218< 0.1%
 
Other Letter38< 0.1%
 
Other Number32< 0.1%
 
Control9< 0.1%
 
Format2< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T1849568.6%
 
S1826038.5%
 
A1733968.1%
 
C1688027.8%
 
P1221425.7%
 
E1138335.3%
 
M1095625.1%
 
B1060474.9%
 
R989634.6%
 
D970104.5%
 
F939214.4%
 
L888864.1%
 
O800773.7%
 
I799503.7%
 
N751163.5%
 
H746843.5%
 
G693133.2%
 
W642063.0%
 
U375191.7%
 
V320881.5%
 
K304461.4%
 
Y277861.3%
 
J214951.0%
 
Z70800.3%
 
Q53770.3%
 
Other values (27)52890.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e113213413.0%
 
a7472018.6%
 
o7335238.5%
 
i6680447.7%
 
r6622187.6%
 
n6333527.3%
 
t6258597.2%
 
s4894775.6%
 
l4517545.2%
 
h3088853.6%
 
d3078063.5%
 
u2910233.4%
 
c2813323.2%
 
m2256062.6%
 
g1899002.2%
 
y1716232.0%
 
p1614961.9%
 
f1349401.6%
 
b1156041.3%
 
k1043861.2%
 
v879471.0%
 
w841281.0%
 
x236580.3%
 
z195060.2%
 
j173110.2%
 
Other values (37)108900.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1780490> 99.9%
 
 53< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:5939719.6%
 
.4800415.8%
 
'4221513.9%
 
!4179813.8%
 
"3978213.1%
 
,3110310.3%
 
&187316.2%
 
/103603.4%
 
?47251.6%
 
#30491.0%
 
*10910.4%
 
;8930.3%
 
@6430.2%
 
%5850.2%
 
3140.1%
 
96< 0.1%
 
\91< 0.1%
 
¡88< 0.1%
 
·73< 0.1%
 
¿17< 0.1%
 
11< 0.1%
 
§5< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-9028798.1%
 
11531.3%
 
6080.7%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(3595197.9%
 
[6791.8%
 
{1030.3%
 
5< 0.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)3613397.9%
 
]6841.9%
 
}1050.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
12332724.0%
 
02027220.9%
 
21987420.5%
 
386869.0%
 
559436.1%
 
457225.9%
 
641904.3%
 
731093.2%
 
830773.2%
 
928022.9%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
159362.1%
 
®77130.1%
 
°1114.3%
 
©893.5%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
|410649.5%
 
+244929.5%
 
~104012.5%
 
=3934.7%
 
>1832.2%
 
<1161.4%
 
×50.1%
 
÷3< 0.1%
 
±1< 0.1%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$78989.5%
 
£576.5%
 
283.2%
 
¢50.6%
 
¥30.3%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_545100.0%
 

Most frequent Initial Punctuation characters

ValueCountFrequency (%) 
29378.6%
 
6016.1%
 
«195.1%
 
10.3%
 

Most frequent Final Punctuation characters

ValueCountFrequency (%) 
85572.6%
 
29925.4%
 
»221.9%
 
10.1%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
´10950.0%
 
`7534.4%
 
^2611.9%
 
¨83.7%
 

Most frequent Other Letter characters

ValueCountFrequency (%) 
º38100.0%
 

Most frequent Other Number characters

ValueCountFrequency (%) 
²2268.8%
 
³825.0%
 
½13.1%
 
¹13.1%
 

Most frequent Format characters

ValueCountFrequency (%) 
­2100.0%
 

Most frequent Control characters

ValueCountFrequency (%) 
555.6%
 
333.3%
 
111.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1083017982.1%
 
Common236043117.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e113213410.5%
 
a7472016.9%
 
o7335236.8%
 
i6680446.2%
 
r6622186.1%
 
n6333525.8%
 
t6258595.8%
 
s4894774.5%
 
l4517544.2%
 
h3088852.9%
 
d3078062.8%
 
u2910232.7%
 
c2813322.6%
 
m2256062.1%
 
g1899001.8%
 
T1849561.7%
 
S1826031.7%
 
A1733961.6%
 
y1716231.6%
 
C1688021.6%
 
p1614961.5%
 
f1349401.2%
 
P1221421.1%
 
b1156041.1%
 
E1138331.1%
 
Other values (89)155267014.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
178049075.4%
 
-902873.8%
 
:593972.5%
 
.480042.0%
 
'422151.8%
 
!417981.8%
 
"397821.7%
 
)361331.5%
 
(359511.5%
 
,311031.3%
 
1233271.0%
 
0202720.9%
 
2198740.8%
 
&187310.8%
 
/103600.4%
 
386860.4%
 
559430.3%
 
457220.2%
 
?47250.2%
 
641900.2%
 
|41060.2%
 
731090.1%
 
830770.1%
 
#30490.1%
 
928020.1%
 
Other values (59)172980.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1317882899.9%
 
None6465< 0.1%
 
Punctuation3696< 0.1%
 
Letterlike Symbols1593< 0.1%
 
Currency Symbols28< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
178049013.5%
 
e11321348.6%
 
a7472015.7%
 
o7335235.6%
 
i6680445.1%
 
r6622185.0%
 
n6333524.8%
 
t6258594.7%
 
s4894773.7%
 
l4517543.4%
 
h3088852.3%
 
d3078062.3%
 
u2910232.2%
 
c2813322.1%
 
m2256061.7%
 
g1899001.4%
 
T1849561.4%
 
S1826031.4%
 
A1733961.3%
 
y1716231.3%
 
C1688021.3%
 
p1614961.2%
 
f1349401.0%
 
P1221420.9%
 
b1156040.9%
 
Other values (73)223466217.0%
 

Most frequent Letterlike Symbols characters

ValueCountFrequency (%) 
1593100.0%
 

Most frequent None characters

ValueCountFrequency (%) 
é138721.5%
 
®77111.9%
 
ó3695.7%
 
ü3545.5%
 
ö2684.1%
 
í2674.1%
 
ä2634.1%
 
á2343.6%
 
ñ2093.2%
 
è1832.8%
 
å1622.5%
 
à1542.4%
 
°1111.7%
 
´1091.7%
 
É1011.6%
 
ú921.4%
 
©891.4%
 
¡881.4%
 
ø831.3%
 
ê751.2%
 
·731.1%
 
æ580.9%
 
£570.9%
 
Ü570.9%
 
 530.8%
 
Other values (61)79812.3%
 

Most frequent Punctuation characters

ValueCountFrequency (%) 
115331.2%
 
85523.1%
 
60816.5%
 
3148.5%
 
2998.1%
 
2937.9%
 
962.6%
 
601.6%
 
110.3%
 
50.1%
 
1< 0.1%
 
1< 0.1%
 

Most frequent Currency Symbols characters

ValueCountFrequency (%) 
28100.0%
 

category
Categorical

HIGH CARDINALITY

Distinct159
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Product Design
 
22314
Documentary
 
16139
Music
 
15727
Tabletop Games
 
14180
Shorts
 
12357
Other values (154)
297944 
ValueCountFrequency (%) 
Product Design223145.9%
 
Documentary161394.3%
 
Music157274.2%
 
Tabletop Games141803.7%
 
Shorts123573.3%
 
Video Games118303.1%
 
Food114933.0%
 
Film & Video101082.7%
 
Fiction91692.4%
 
Fashion85542.3%
 
Nonfiction83182.2%
 
Art82532.2%
 
Apparel71661.9%
 
Theater70571.9%
 
Technology69301.8%
 
Rock67581.8%
 
Children's Books67561.8%
 
Apps63451.7%
 
Publishing60181.6%
 
Webseries57621.5%
 
Photography57521.5%
 
Indie Rock56571.5%
 
Narrative Film51881.4%
 
Web51531.4%
 
Comics49961.3%
 
Other values (134)15068139.8%
 
2020-11-22T06:48:03.217557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-22T06:48:03.401816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length9
Mean length9.045219867
Min length3

Overview of Unicode Properties

Unique unicode characters53
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o2998698.8%
 
e2682137.8%
 
i2595447.6%
 
s2238086.5%
 
a2009485.9%
 
r1974745.8%
 
t1867885.5%
 
n1637654.8%
 
c1550374.5%
 
1406504.1%
 
l1160053.4%
 
d1007072.9%
 
u969052.8%
 
m928052.7%
 
h717572.1%
 
p694932.0%
 
g635211.9%
 
P604571.8%
 
F581711.7%
 
D563681.6%
 
y448841.3%
 
A434091.3%
 
b409151.2%
 
G397701.2%
 
k381851.1%
 
Other values (28)3356249.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter274263080.1%
 
Uppercase Letter51234915.0%
 
Space Separator1406504.1%
 
Other Punctuation231200.7%
 
Dash Punctuation56400.2%
 
Decimal Number683< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P6045711.8%
 
F5817111.4%
 
D5636811.0%
 
A434098.5%
 
G397707.8%
 
C356317.0%
 
M331486.5%
 
T327126.4%
 
V238574.7%
 
S218464.3%
 
R183103.6%
 
W163223.2%
 
N159443.1%
 
B149302.9%
 
H132022.6%
 
I121172.4%
 
E56661.1%
 
J51141.0%
 
Y28960.6%
 
L15410.3%
 
K4660.1%
 
Z3910.1%
 
Q81< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o29986910.9%
 
e2682139.8%
 
i2595449.5%
 
s2238088.2%
 
a2009487.3%
 
r1974747.2%
 
t1867886.8%
 
n1637656.0%
 
c1550375.7%
 
l1160054.2%
 
d1007073.7%
 
u969053.5%
 
m928053.4%
 
h717572.6%
 
p694932.5%
 
g635212.3%
 
y448841.6%
 
b409151.5%
 
k381851.4%
 
f191970.7%
 
v121160.4%
 
w118230.4%
 
x42930.2%
 
z41620.2%
 
q416< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
140650100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
&1594068.9%
 
'718031.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-5640100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3683100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin325497995.0%
 
Common1700935.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o2998699.2%
 
e2682138.2%
 
i2595448.0%
 
s2238086.9%
 
a2009486.2%
 
r1974746.1%
 
t1867885.7%
 
n1637655.0%
 
c1550374.8%
 
l1160053.6%
 
d1007073.1%
 
u969053.0%
 
m928052.9%
 
h717572.2%
 
p694932.1%
 
g635212.0%
 
P604571.9%
 
F581711.8%
 
D563681.7%
 
y448841.4%
 
A434091.3%
 
b409151.3%
 
G397701.2%
 
k381851.2%
 
C356311.1%
 
Other values (23)2705508.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
14065082.7%
 
&159409.4%
 
'71804.2%
 
-56403.3%
 
36830.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3425072100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o2998698.8%
 
e2682137.8%
 
i2595447.6%
 
s2238086.5%
 
a2009485.9%
 
r1974745.8%
 
t1867885.5%
 
n1637654.8%
 
c1550374.5%
 
1406504.1%
 
l1160053.4%
 
d1007072.9%
 
u969052.8%
 
m928052.7%
 
h717572.1%
 
p694932.0%
 
g635211.9%
 
P604571.8%
 
F581711.7%
 
D563681.6%
 
y448841.3%
 
A434091.3%
 
b409151.2%
 
G397701.2%
 
k381851.1%
 
Other values (28)3356249.8%
 

main_category
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Film & Video
63585 
Music
51918 
Publishing
39874 
Games
35231 
Technology
32569 
Other values (10)
155484 
ValueCountFrequency (%) 
Film & Video6358516.8%
 
Music5191813.7%
 
Publishing3987410.5%
 
Games352319.3%
 
Technology325698.6%
 
Design300707.9%
 
Art281537.4%
 
Food246026.5%
 
Fashion228166.0%
 
Theater109132.9%
 
Comics108192.9%
 
Photography107792.8%
 
Crafts88092.3%
 
Journalism47551.3%
 
Dance37681.0%
 
2020-11-22T06:48:03.584998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-22T06:48:03.742376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length6
Mean length7.461322925
Min length3

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i32729611.6%
 
o2378758.4%
 
s2042927.2%
 
e1870496.6%
 
l1407835.0%
 
n1338524.7%
 
h1277304.5%
 
1271704.5%
 
m1143904.0%
 
g1132924.0%
 
F1110033.9%
 
c990743.5%
 
a970713.4%
 
u965473.4%
 
d881873.1%
 
&635852.3%
 
V635852.3%
 
r634092.2%
 
t586542.1%
 
M519181.8%
 
P506531.8%
 
T434821.5%
 
y433481.5%
 
b398741.4%
 
G352311.2%
 
Other values (6)1059623.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter219231177.6%
 
Uppercase Letter44224615.7%
 
Space Separator1271704.5%
 
Other Punctuation635852.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F11100325.1%
 
V6358514.4%
 
M5191811.7%
 
P5065311.5%
 
T434829.8%
 
G352318.0%
 
D338387.7%
 
A281536.4%
 
C196284.4%
 
J47551.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i32729614.9%
 
o23787510.9%
 
s2042929.3%
 
e1870498.5%
 
l1407836.4%
 
n1338526.1%
 
h1277305.8%
 
m1143905.2%
 
g1132925.2%
 
c990744.5%
 
a970714.4%
 
u965474.4%
 
d881874.0%
 
r634092.9%
 
t586542.7%
 
y433482.0%
 
b398741.8%
 
p107790.5%
 
f88090.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
127170100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
&63585100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin263455793.2%
 
Common1907556.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i32729612.4%
 
o2378759.0%
 
s2042927.8%
 
e1870497.1%
 
l1407835.3%
 
n1338525.1%
 
h1277304.8%
 
m1143904.3%
 
g1132924.3%
 
F1110034.2%
 
c990743.8%
 
a970713.7%
 
u965473.7%
 
d881873.3%
 
V635852.4%
 
r634092.4%
 
t586542.2%
 
M519182.0%
 
P506531.9%
 
T434821.7%
 
y433481.6%
 
b398741.5%
 
G352311.3%
 
D338381.3%
 
A281531.1%
 
Other values (4)439711.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
12717066.7%
 
&6358533.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2825312100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i32729611.6%
 
o2378758.4%
 
s2042927.2%
 
e1870496.6%
 
l1407835.0%
 
n1338524.7%
 
h1277304.5%
 
1271704.5%
 
m1143904.0%
 
g1132924.0%
 
F1110033.9%
 
c990743.5%
 
a970713.4%
 
u965473.4%
 
d881873.1%
 
&635852.3%
 
V635852.3%
 
r634092.2%
 
t586542.1%
 
M519181.8%
 
P506531.8%
 
T434821.5%
 
y433481.5%
 
b398741.4%
 
G352311.2%
 
Other values (6)1059623.8%
 

currency
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
USD
295365 
GBP
34132 
EUR
 
17405
CAD
 
14962
AUD
 
7950
Other values (9)
 
8847
ValueCountFrequency (%) 
USD29536578.0%
 
GBP341329.0%
 
EUR174054.6%
 
CAD149624.0%
 
AUD79502.1%
 
SEK17880.5%
 
MXN17520.5%
 
NZD14750.4%
 
DKK11290.3%
 
CHF7680.2%
 
NOK7220.2%
 
HKD6180.2%
 
SGD5550.1%
 
JPY40< 0.1%
 
2020-11-22T06:48:03.904236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-22T06:48:04.062273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
D32205428.4%
 
U32072028.2%
 
S29770826.2%
 
G346873.1%
 
P341723.0%
 
B341323.0%
 
A229122.0%
 
E191931.7%
 
R174051.5%
 
C157301.4%
 
K53860.5%
 
N39490.3%
 
M17520.2%
 
X17520.2%
 
Z14750.1%
 
H13860.1%
 
F7680.1%
 
O7220.1%
 
J40< 0.1%
 
Y40< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1135983100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D32205428.4%
 
U32072028.2%
 
S29770826.2%
 
G346873.1%
 
P341723.0%
 
B341323.0%
 
A229122.0%
 
E191931.7%
 
R174051.5%
 
C157301.4%
 
K53860.5%
 
N39490.3%
 
M17520.2%
 
X17520.2%
 
Z14750.1%
 
H13860.1%
 
F7680.1%
 
O7220.1%
 
J40< 0.1%
 
Y40< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1135983100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
D32205428.4%
 
U32072028.2%
 
S29770826.2%
 
G346873.1%
 
P341723.0%
 
B341323.0%
 
A229122.0%
 
E191931.7%
 
R174051.5%
 
C157301.4%
 
K53860.5%
 
N39490.3%
 
M17520.2%
 
X17520.2%
 
Z14750.1%
 
H13860.1%
 
F7680.1%
 
O7220.1%
 
J40< 0.1%
 
Y40< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1135983100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
D32205428.4%
 
U32072028.2%
 
S29770826.2%
 
G346873.1%
 
P341723.0%
 
B341323.0%
 
A229122.0%
 
E191931.7%
 
R174051.5%
 
C157301.4%
 
K53860.5%
 
N39490.3%
 
M17520.2%
 
X17520.2%
 
Z14750.1%
 
H13860.1%
 
F7680.1%
 
O7220.1%
 
J40< 0.1%
 
Y40< 0.1%
 

deadline
Categorical

HIGH CARDINALITY

Distinct3164
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
2014-08-08
 
705
2014-08-10
 
558
2014-08-07
 
541
2015-05-01
 
489
2014-08-09
 
477
Other values (3159)
375891 
ValueCountFrequency (%) 
2014-08-087050.2%
 
2014-08-105580.1%
 
2014-08-075410.1%
 
2015-05-014890.1%
 
2014-08-094770.1%
 
2015-07-014490.1%
 
2015-04-014300.1%
 
2014-08-154230.1%
 
2014-08-314200.1%
 
2014-08-144130.1%
 
2014-08-203940.1%
 
2014-08-133940.1%
 
2015-04-303910.1%
 
2014-11-013790.1%
 
2015-01-013780.1%
 
2015-10-013750.1%
 
2014-08-163610.1%
 
2015-05-313590.1%
 
2017-12-013560.1%
 
2015-03-013550.1%
 
2015-08-013490.1%
 
2014-09-073440.1%
 
2014-09-123440.1%
 
2015-06-013430.1%
 
2014-10-313430.1%
 
Other values (3139)36829197.3%
 
2020-11-22T06:48:04.260721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique38 ?
Unique (%)< 0.1%
2020-11-22T06:48:04.439053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
085918222.7%
 
-75732220.0%
 
173535519.4%
 
262832016.6%
 
51484773.9%
 
31345393.6%
 
41338303.5%
 
61265453.3%
 
71214173.2%
 
8747392.0%
 
9668841.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number302928880.0%
 
Dash Punctuation75732220.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
085918228.4%
 
173535524.3%
 
262832020.7%
 
51484774.9%
 
31345394.4%
 
41338304.4%
 
61265454.2%
 
71214174.0%
 
8747392.5%
 
9668842.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-757322100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3786610100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
085918222.7%
 
-75732220.0%
 
173535519.4%
 
262832016.6%
 
51484773.9%
 
31345393.6%
 
41338303.5%
 
61265453.3%
 
71214173.2%
 
8747392.0%
 
9668841.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3786610100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
085918222.7%
 
-75732220.0%
 
173535519.4%
 
262832016.6%
 
51484773.9%
 
31345393.6%
 
41338303.5%
 
61265453.3%
 
71214173.2%
 
8747392.0%
 
9668841.8%
 

goal
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct8353
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49080.79152
Minimum0.01
Maximum100000000
Zeros0
Zeros (%)0.0%
Memory size2.9 MiB
2020-11-22T06:48:04.610397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile400
Q12000
median5200
Q316000
95-th percentile90000
Maximum100000000
Range99999999.99
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation1183391.259
Coefficient of variation (CV)24.1110875
Kurtosis5574.951599
Mean49080.79152
Median Absolute Deviation (MAD)4460
Skewness70.79927974
Sum1.85849816e+10
Variance1.400414872e+12
MonotocityNot monotonic
2020-11-22T06:48:04.811371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5000297797.9%
 
10000260296.9%
 
1000169504.5%
 
3000157434.2%
 
2000152574.0%
 
15000142313.8%
 
20000131043.5%
 
2500118643.1%
 
500115993.1%
 
25000103712.7%
 
1500103142.7%
 
5000095712.5%
 
400083102.2%
 
3000077822.1%
 
600074962.0%
 
350067911.8%
 
800064791.7%
 
700050761.3%
 
1200049871.3%
 
10000048621.3%
 
750048141.3%
 
3500035930.9%
 
4000034280.9%
 
30031960.8%
 
120030310.8%
 
Other values (8328)12400432.7%
 
ValueCountFrequency (%) 
0.012< 0.1%
 
0.151< 0.1%
 
0.51< 0.1%
 
14300.1%
 
1.851< 0.1%
 
224< 0.1%
 
320< 0.1%
 
49< 0.1%
 
5142< 0.1%
 
69< 0.1%
 
ValueCountFrequency (%) 
10000000036< 0.1%
 
990000002< 0.1%
 
800000002< 0.1%
 
750000001< 0.1%
 
730000001< 0.1%
 
700000001< 0.1%
 
600000001< 0.1%
 
580000001< 0.1%
 
550000003< 0.1%
 
5000000014< 0.1%
 

launched
Categorical

HIGH CARDINALITY
UNIFORM

Distinct378089
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
1970-01-01 01:00:00
 
7
2016-02-10 17:30:31
 
2
2013-05-09 19:13:19
 
2
2015-08-01 04:52:35
 
2
2015-02-19 18:05:04
 
2
Other values (378084)
378646 
ValueCountFrequency (%) 
1970-01-01 01:00:007< 0.1%
 
2016-02-10 17:30:312< 0.1%
 
2013-05-09 19:13:192< 0.1%
 
2015-08-01 04:52:352< 0.1%
 
2015-02-19 18:05:042< 0.1%
 
2016-08-15 19:02:402< 0.1%
 
2015-09-11 18:36:422< 0.1%
 
2013-04-24 18:59:522< 0.1%
 
2012-03-27 21:18:002< 0.1%
 
2014-02-14 21:26:172< 0.1%
 
2015-09-01 18:00:192< 0.1%
 
2014-08-27 02:09:272< 0.1%
 
2016-03-16 16:35:492< 0.1%
 
2015-01-20 22:22:332< 0.1%
 
2015-08-31 15:49:012< 0.1%
 
2015-01-21 00:16:102< 0.1%
 
2016-10-24 19:11:042< 0.1%
 
2015-11-19 16:31:372< 0.1%
 
2014-07-11 20:07:592< 0.1%
 
2015-08-04 20:32:202< 0.1%
 
2017-11-02 19:01:112< 0.1%
 
2017-03-20 20:11:002< 0.1%
 
2015-05-26 18:00:382< 0.1%
 
2017-07-17 23:12:442< 0.1%
 
2016-05-24 23:33:012< 0.1%
 
Other values (378064)378606> 99.9%
 
2020-11-22T06:48:07.577139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique377522 ?
Unique (%)99.7%
2020-11-22T06:48:07.819388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories4 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0127187117.7%
 
1112966115.7%
 
297096413.5%
 
-75732210.5%
 
:75732210.5%
 
33786665.3%
 
3786615.3%
 
53745505.2%
 
43574595.0%
 
72321413.2%
 
62305533.2%
 
81781022.5%
 
91772872.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number530125473.7%
 
Dash Punctuation75732210.5%
 
Other Punctuation75732210.5%
 
Space Separator3786615.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0127187124.0%
 
1112966121.3%
 
297096418.3%
 
33786667.1%
 
53745507.1%
 
43574596.7%
 
72321414.4%
 
62305534.3%
 
81781023.4%
 
91772873.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-757322100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
378661100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:757322100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common7194559100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0127187117.7%
 
1112966115.7%
 
297096413.5%
 
-75732210.5%
 
:75732210.5%
 
33786665.3%
 
3786615.3%
 
53745505.2%
 
43574595.0%
 
72321413.2%
 
62305533.2%
 
81781022.5%
 
91772872.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7194559100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0127187117.7%
 
1112966115.7%
 
297096413.5%
 
-75732210.5%
 
:75732210.5%
 
33786665.3%
 
3786615.3%
 
53745505.2%
 
43574595.0%
 
72321413.2%
 
62305533.2%
 
81781022.5%
 
91772872.5%
 

pledged
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct62130
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9682.979339
Minimum0
Maximum20338986.27
Zeros52527
Zeros (%)13.9%
Memory size2.9 MiB
2020-11-22T06:48:08.094404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median620
Q34076
95-th percentile29581
Maximum20338986.27
Range20338986.27
Interquartile range (IQR)4046

Descriptive statistics

Standard deviation95636.01
Coefficient of variation (CV)9.876713215
Kurtosis10010.26248
Mean9682.979339
Median Absolute Deviation (MAD)620
Skewness75.1517915
Sum3666566640
Variance9146246410
MonotocityNot monotonic
2020-11-22T06:48:08.740680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05252713.9%
 
191952.4%
 
1050341.3%
 
2539941.1%
 
5036261.0%
 
536101.0%
 
2032200.9%
 
10030620.8%
 
224340.6%
 
3021290.6%
 
1517380.5%
 
4014590.4%
 
6014510.4%
 
3513920.4%
 
1112920.3%
 
7512290.3%
 
15011770.3%
 
20011540.3%
 
611090.3%
 
2610150.3%
 
709850.3%
 
1259480.3%
 
559460.2%
 
1109290.2%
 
518980.2%
 
Other values (62105)27210871.9%
 
ValueCountFrequency (%) 
05252713.9%
 
191952.4%
 
1.015< 0.1%
 
1.023< 0.1%
 
1.033< 0.1%
 
1.041< 0.1%
 
1.051< 0.1%
 
1.071< 0.1%
 
1.082< 0.1%
 
1.118< 0.1%
 
ValueCountFrequency (%) 
20338986.271< 0.1%
 
13285226.361< 0.1%
 
12779843.491< 0.1%
 
12393139.691< 0.1%
 
10266845.741< 0.1%
 
100352961< 0.1%
 
9192055.661< 0.1%
 
8782571.991< 0.1%
 
8596474.581< 0.1%
 
70727571< 0.1%
 

state
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
failed
197719 
successful
133956 
canceled
38779 
undefined
 
3562
live
 
2799
ValueCountFrequency (%) 
failed19771952.2%
 
successful13395635.4%
 
canceled3877910.2%
 
undefined35620.9%
 
live27990.7%
 
suspended18460.5%
 
2020-11-22T06:48:09.170284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-22T06:48:09.341453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:48:09.527485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length6
Mean length7.647933112
Min length4

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e42284814.6%
 
s40556014.0%
 
l37325312.9%
 
c34547011.9%
 
f33523711.6%
 
u2733209.4%
 
d2473148.5%
 
a2364988.2%
 
i2040807.0%
 
n477491.6%
 
v27990.1%
 
p18460.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2895974100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e42284814.6%
 
s40556014.0%
 
l37325312.9%
 
c34547011.9%
 
f33523711.6%
 
u2733209.4%
 
d2473148.5%
 
a2364988.2%
 
i2040807.0%
 
n477491.6%
 
v27990.1%
 
p18460.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2895974100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e42284814.6%
 
s40556014.0%
 
l37325312.9%
 
c34547011.9%
 
f33523711.6%
 
u2733209.4%
 
d2473148.5%
 
a2364988.2%
 
i2040807.0%
 
n477491.6%
 
v27990.1%
 
p18460.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2895974100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e42284814.6%
 
s40556014.0%
 
l37325312.9%
 
c34547011.9%
 
f33523711.6%
 
u2733209.4%
 
d2473148.5%
 
a2364988.2%
 
i2040807.0%
 
n477491.6%
 
v27990.1%
 
p18460.1%
 

backers
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct3963
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.6174758
Minimum0
Maximum219382
Zeros55609
Zeros (%)14.7%
Memory size2.9 MiB
2020-11-22T06:48:09.977276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q356
95-th percentile334
Maximum219382
Range219382
Interquartile range (IQR)54

Descriptive statistics

Standard deviation907.1850348
Coefficient of variation (CV)8.58934592
Kurtosis13954.93808
Mean105.6174758
Median Absolute Deviation (MAD)12
Skewness86.76300901
Sum39993219
Variance822984.6874
MonotocityNot monotonic
2020-11-22T06:48:10.192233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05560914.7%
 
1348699.2%
 
2231966.1%
 
3160634.2%
 
4120683.2%
 
597162.6%
 
681372.1%
 
770141.9%
 
861981.6%
 
955531.5%
 
1050681.3%
 
1145471.2%
 
1243481.1%
 
1341361.1%
 
1439501.0%
 
1537341.0%
 
1634470.9%
 
1734400.9%
 
1831640.8%
 
1931480.8%
 
2029590.8%
 
2128220.7%
 
2227310.7%
 
2326480.7%
 
2525430.7%
 
Other values (3938)14755339.0%
 
ValueCountFrequency (%) 
05560914.7%
 
1348699.2%
 
2231966.1%
 
3160634.2%
 
4120683.2%
 
597162.6%
 
681372.1%
 
770141.9%
 
861981.6%
 
955531.5%
 
ValueCountFrequency (%) 
2193821< 0.1%
 
1549261< 0.1%
 
1058571< 0.1%
 
915851< 0.1%
 
871421< 0.1%
 
855811< 0.1%
 
784711< 0.1%
 
744051< 0.1%
 
739861< 0.1%
 
732061< 0.1%
 

country
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
US
292627 
GB
33672 
CA
 
14756
AU
 
7839
DE
 
4171
Other values (18)
 
25596
ValueCountFrequency (%) 
US29262777.3%
 
GB336728.9%
 
CA147563.9%
 
AU78392.1%
 
DE41711.1%
 
N,0"37971.0%
 
FR29390.8%
 
IT28780.8%
 
NL28680.8%
 
ES22760.6%
 
SE17570.5%
 
MX17520.5%
 
NZ14470.4%
 
DK11130.3%
 
IE8110.2%
 
CH7610.2%
 
NO7080.2%
 
HK6180.2%
 
BE6170.2%
 
AT5970.2%
 
SG5550.1%
 
LU62< 0.1%
 
JP40< 0.1%
 
2020-11-22T06:48:10.431097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-22T06:48:10.653421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.020054878
Min length2

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
U30052839.3%
 
S29721538.9%
 
B342894.5%
 
G342274.5%
 
A231923.0%
 
C155172.0%
 
E96321.3%
 
N88201.2%
 
D52840.7%
 
,37970.5%
 
037970.5%
 
"37970.5%
 
I36890.5%
 
T34750.5%
 
F29390.4%
 
R29390.4%
 
L29300.4%
 
M17520.2%
 
X17520.2%
 
K17310.2%
 
Z14470.2%
 
H13790.2%
 
O7080.1%
 
J40< 0.1%
 
P40< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter75352598.5%
 
Other Punctuation75941.0%
 
Decimal Number37970.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U30052839.9%
 
S29721539.4%
 
B342894.6%
 
G342274.5%
 
A231923.1%
 
C155172.1%
 
E96321.3%
 
N88201.2%
 
D52840.7%
 
I36890.5%
 
T34750.5%
 
F29390.4%
 
R29390.4%
 
L29300.4%
 
M17520.2%
 
X17520.2%
 
K17310.2%
 
Z14470.2%
 
H13790.2%
 
O7080.1%
 
J40< 0.1%
 
P40< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,379750.0%
 
"379750.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
03797100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin75352598.5%
 
Common113911.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U30052839.9%
 
S29721539.4%
 
B342894.6%
 
G342274.5%
 
A231923.1%
 
C155172.1%
 
E96321.3%
 
N88201.2%
 
D52840.7%
 
I36890.5%
 
T34750.5%
 
F29390.4%
 
R29390.4%
 
L29300.4%
 
M17520.2%
 
X17520.2%
 
K17310.2%
 
Z14470.2%
 
H13790.2%
 
O7080.1%
 
J40< 0.1%
 
P40< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
,379733.3%
 
0379733.3%
 
"379733.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII764916100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
U30052839.3%
 
S29721538.9%
 
B342894.5%
 
G342274.5%
 
A231923.0%
 
C155172.0%
 
E96321.3%
 
N88201.2%
 
D52840.7%
 
,37970.5%
 
037970.5%
 
"37970.5%
 
I36890.5%
 
T34750.5%
 
F29390.4%
 
R29390.4%
 
L29300.4%
 
M17520.2%
 
X17520.2%
 
K17310.2%
 
Z14470.2%
 
H13790.2%
 
O7080.1%
 
J40< 0.1%
 
P40< 0.1%
 

usd pledged
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct95455
Distinct (%)25.5%
Missing3797
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean7036.728867
Minimum0
Maximum20338986.27
Zeros68112
Zeros (%)18.0%
Memory size2.9 MiB
2020-11-22T06:48:11.013028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.98
median394.72
Q33034.09
95-th percentile22432.85
Maximum20338986.27
Range20338986.27
Interquartile range (IQR)3017.11

Descriptive statistics

Standard deviation78639.74531
Coefficient of variation (CV)11.17561111
Kurtosis18960.92214
Mean7036.728867
Median Absolute Deviation (MAD)394.72
Skewness105.8999201
Sum2637816330
Variance6184209542
MonotocityNot monotonic
2020-11-22T06:48:11.339190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06811218.0%
 
153411.4%
 
2538771.0%
 
1036241.0%
 
5031410.8%
 
10026740.7%
 
525980.7%
 
2024730.7%
 
3017170.5%
 
214510.4%
 
1513100.3%
 
4011890.3%
 
6011840.3%
 
3511790.3%
 
7511200.3%
 
15010650.3%
 
2009890.3%
 
1259170.2%
 
707910.2%
 
557770.2%
 
1107730.2%
 
457580.2%
 
117360.2%
 
807110.2%
 
267060.2%
 
Other values (95430)26565170.2%
 
(Missing)37971.0%
 
ValueCountFrequency (%) 
06811218.0%
 
0.473< 0.1%
 
0.481< 0.1%
 
0.511< 0.1%
 
0.523< 0.1%
 
0.531< 0.1%
 
0.541< 0.1%
 
0.551< 0.1%
 
0.563< 0.1%
 
0.574< 0.1%
 
ValueCountFrequency (%) 
20338986.271< 0.1%
 
13285226.361< 0.1%
 
12779843.491< 0.1%
 
10266845.741< 0.1%
 
9192055.661< 0.1%
 
8782571.991< 0.1%
 
8596474.581< 0.1%
 
6333295.771< 0.1%
 
6225354.981< 0.1%
 
5764229.381< 0.1%
 

usd_pledged_real
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct106065
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9058.924074
Minimum0
Maximum20338986.27
Zeros52527
Zeros (%)13.9%
Memory size2.9 MiB
2020-11-22T06:48:11.793104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median624.33
Q34050
95-th percentile28090
Maximum20338986.27
Range20338986.27
Interquartile range (IQR)4019

Descriptive statistics

Standard deviation90973.34311
Coefficient of variation (CV)10.04240044
Kurtosis11796.48194
Mean9058.924074
Median Absolute Deviation (MAD)624.33
Skewness82.18751606
Sum3430261249
Variance8276149156
MonotocityNot monotonic
2020-11-22T06:48:12.050269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05252713.9%
 
166781.8%
 
1036331.0%
 
2534550.9%
 
5029370.8%
 
525840.7%
 
10024610.6%
 
2023540.6%
 
217000.4%
 
3016550.4%
 
1512800.3%
 
6011280.3%
 
3511260.3%
 
4011060.3%
 
7510320.3%
 
1509610.3%
 
2009060.2%
 
119050.2%
 
1258380.2%
 
268320.2%
 
67740.2%
 
557430.2%
 
707400.2%
 
1107200.2%
 
516880.2%
 
Other values (106040)28489875.2%
 
ValueCountFrequency (%) 
05252713.9%
 
0.451< 0.1%
 
0.471< 0.1%
 
0.482< 0.1%
 
0.495< 0.1%
 
0.511< 0.1%
 
0.529< 0.1%
 
0.5342< 0.1%
 
0.546< 0.1%
 
0.5514< 0.1%
 
ValueCountFrequency (%) 
20338986.271< 0.1%
 
13285226.361< 0.1%
 
12779843.491< 0.1%
 
12393139.691< 0.1%
 
10266845.741< 0.1%
 
9192055.661< 0.1%
 
8782571.991< 0.1%
 
8596474.581< 0.1%
 
70727571< 0.1%
 
6565782.51< 0.1%
 

usd_goal_real
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct50339
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45454.40147
Minimum0.01
Maximum166361390.7
Zeros0
Zeros (%)0.0%
Memory size2.9 MiB
2020-11-22T06:48:12.335704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile400
Q12000
median5500
Q315500
95-th percentile80000
Maximum166361390.7
Range166361390.7
Interquartile range (IQR)13500

Descriptive statistics

Standard deviation1152950.055
Coefficient of variation (CV)25.36498156
Kurtosis7082.887238
Mean45454.40147
Median Absolute Deviation (MAD)4500
Skewness78.22084847
Sum1.721180911e+10
Variance1.32929383e+12
MonotocityNot monotonic
2020-11-22T06:48:12.596916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5000241736.4%
 
10000207865.5%
 
1000130293.4%
 
3000126993.4%
 
2000119153.1%
 
15000113743.0%
 
20000101212.7%
 
250098492.6%
 
50085882.3%
 
2500083642.2%
 
150081712.2%
 
5000072301.9%
 
400066881.8%
 
600059831.6%
 
3000059081.6%
 
350056951.5%
 
800052151.4%
 
700041151.1%
 
750040761.1%
 
1200040741.1%
 
10000034800.9%
 
3500027090.7%
 
120024930.7%
 
4000024760.7%
 
550023820.6%
 
Other values (50314)17706846.8%
 
ValueCountFrequency (%) 
0.012< 0.1%
 
0.151< 0.1%
 
0.491< 0.1%
 
0.51< 0.1%
 
0.551< 0.1%
 
0.581< 0.1%
 
0.721< 0.1%
 
0.731< 0.1%
 
0.743< 0.1%
 
0.756< 0.1%
 
ValueCountFrequency (%) 
166361390.71< 0.1%
 
151395869.91< 0.1%
 
110169771.61< 0.1%
 
107369867.71< 0.1%
 
104057189.81< 0.1%
 
10000000024< 0.1%
 
990000001< 0.1%
 
88767573.211< 0.1%
 
87092840.971< 0.1%
 
84300785.21< 0.1%
 

inicio
Date

Distinct378089
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Minimum1970-01-01 01:00:00
Maximum2018-01-02 15:02:31
2020-11-22T06:48:12.839207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:48:13.115257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

final
Date

Distinct3164
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Minimum2009-05-03 00:00:00
Maximum2018-03-03 00:00:00
2020-11-22T06:48:13.335264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:48:13.539560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

duracion
Categorical

HIGH CARDINALITY
UNIFORM

Distinct262415
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
29 days 05:59:56
 
17
29 days 05:59:44
 
16
29 days 05:59:26
 
15
29 days 05:59:50
 
15
29 days 05:59:09
 
14
Other values (262410)
378584 
ValueCountFrequency (%) 
29 days 05:59:5617< 0.1%
 
29 days 05:59:4416< 0.1%
 
29 days 05:59:2615< 0.1%
 
29 days 05:59:5015< 0.1%
 
29 days 05:59:0914< 0.1%
 
29 days 05:58:4814< 0.1%
 
29 days 05:59:4314< 0.1%
 
29 days 04:59:3114< 0.1%
 
29 days 05:59:3513< 0.1%
 
29 days 06:59:4713< 0.1%
 
29 days 05:59:2813< 0.1%
 
29 days 05:59:5213< 0.1%
 
29 days 05:08:3013< 0.1%
 
29 days 05:59:3113< 0.1%
 
29 days 22:59:5813< 0.1%
 
29 days 02:59:2813< 0.1%
 
29 days 05:59:3013< 0.1%
 
29 days 05:59:4113< 0.1%
 
29 days 06:59:5213< 0.1%
 
29 days 02:27:2212< 0.1%
 
29 days 05:58:5012< 0.1%
 
29 days 00:58:5612< 0.1%
 
29 days 00:59:3312< 0.1%
 
29 days 07:59:5012< 0.1%
 
29 days 04:59:4212< 0.1%
 
Other values (262390)37832799.9%
 
2020-11-22T06:48:14.004948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique203936 ?
Unique (%)53.9%
2020-11-22T06:48:14.324566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length16
Mean length15.98199709
Min length15

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
75732212.5%
 
:75732212.5%
 
25401798.9%
 
04949588.2%
 
d3786616.3%
 
a3786616.3%
 
y3786616.3%
 
s3786616.3%
 
13674806.1%
 
93319665.5%
 
33215215.3%
 
43056035.0%
 
53009805.0%
 
61206062.0%
 
81202332.0%
 
71189452.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number302247149.9%
 
Lowercase Letter151464425.0%
 
Space Separator75732212.5%
 
Other Punctuation75732212.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
254017917.9%
 
049495816.4%
 
136748012.2%
 
933196611.0%
 
332152110.6%
 
430560310.1%
 
530098010.0%
 
61206064.0%
 
81202334.0%
 
71189453.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
757322100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d37866125.0%
 
a37866125.0%
 
y37866125.0%
 
s37866125.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:757322100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common453711575.0%
 
Latin151464425.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
75732216.7%
 
:75732216.7%
 
254017911.9%
 
049495810.9%
 
13674808.1%
 
93319667.3%
 
33215217.1%
 
43056036.7%
 
53009806.6%
 
61206062.7%
 
81202332.6%
 
71189452.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d37866125.0%
 
a37866125.0%
 
y37866125.0%
 
s37866125.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6051759100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
75732212.5%
 
:75732212.5%
 
25401798.9%
 
04949588.2%
 
d3786616.3%
 
a3786616.3%
 
y3786616.3%
 
s3786616.3%
 
13674806.1%
 
93319665.5%
 
33215215.3%
 
43056035.0%
 
53009805.0%
 
61206062.0%
 
81202332.0%
 
71189452.0%
 

Interactions

2020-11-22T06:47:37.687081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:38.030049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:38.300638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:38.539532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:38.759307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:38.998963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:39.267072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:39.947264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:40.265712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:40.580638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:40.851417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:41.103421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:41.384718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:41.665735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:41.933845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:42.206435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:42.485052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:42.772327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:43.041921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:43.498640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:43.785565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:44.073901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:44.310619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:44.556622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:44.811810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:45.051387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:45.309559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:45.570513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:45.825162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:46.175722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:46.511973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:46.776668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:47.023888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:47.286718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:47.552024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:47.807380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:48.052228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:48.314863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:48.653080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:48.920073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:49.213792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:49.489012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:49.745639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:49.982594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:50.225045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:50.483571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:50.728902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:50.980986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:51.233937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-22T06:48:14.550393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-22T06:48:14.794911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-22T06:48:15.020337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-22T06:48:15.299375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-22T06:48:15.529490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-22T06:47:53.539745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:55.357576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:57.646308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-22T06:47:58.132513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

IDnamecategorymain_categorycurrencydeadlinegoallaunchedpledgedstatebackerscountryusd pledgedusd_pledged_realusd_goal_realiniciofinalduracion
01000002330The Songs of Adelaide & AbullahPoetryPublishingGBP2015-10-091000.02015-08-11 12:12:280.00failed0GB0.000.001533.952015-08-11 12:12:282015-10-0958 days 11:47:32
11000003930Greeting From Earth: ZGAC Arts Capsule For ETNarrative FilmFilm & VideoUSD2017-11-0130000.02017-09-02 04:43:572421.00failed15US100.002421.0030000.002017-09-02 04:43:572017-11-0159 days 19:16:03
21000004038Where is Hank?Narrative FilmFilm & VideoUSD2013-02-2645000.02013-01-12 00:20:50220.00failed3US220.00220.0045000.002013-01-12 00:20:502013-02-2644 days 23:39:10
31000007540ToshiCapital Rekordz Needs Help to Complete AlbumMusicMusicUSD2012-04-165000.02012-03-17 03:24:111.00failed1US1.001.005000.002012-03-17 03:24:112012-04-1629 days 20:35:49
41000011046Community Film Project: The Art of Neighborhood FilmmakingFilm & VideoFilm & VideoUSD2015-08-2919500.02015-07-04 08:35:031283.00canceled14US1283.001283.0019500.002015-07-04 08:35:032015-08-2955 days 15:24:57
51000014025Monarch Espresso BarRestaurantsFoodUSD2016-04-0150000.02016-02-26 13:38:2752375.00successful224US52375.0052375.0050000.002016-02-26 13:38:272016-04-0134 days 10:21:33
61000023410Support Solar Roasted Coffee & Green Energy! SolarCoffee.coFoodFoodUSD2014-12-211000.02014-12-01 18:30:441205.00successful16US1205.001205.001000.002014-12-01 18:30:442014-12-2119 days 05:29:16
71000030581Chaser Strips. Our Strips make Shots their B*tch!DrinksFoodUSD2016-03-1725000.02016-02-01 20:05:12453.00failed40US453.00453.0025000.002016-02-01 20:05:122016-03-1744 days 03:54:48
81000034518SPIN - Premium Retractable In-Ear Headphones with MicProduct DesignDesignUSD2014-05-29125000.02014-04-24 18:14:438233.00canceled58US8233.008233.00125000.002014-04-24 18:14:432014-05-2934 days 05:45:17
9100004195STUDIO IN THE SKY - A Documentary Feature Film (Canceled)DocumentaryFilm & VideoUSD2014-08-1065000.02014-07-11 21:55:486240.57canceled43US6240.576240.5765000.002014-07-11 21:55:482014-08-1029 days 02:04:12

Last rows

IDnamecategorymain_categorycurrencydeadlinegoallaunchedpledgedstatebackerscountryusd pledgedusd_pledged_realusd_goal_realiniciofinalduracion
378651999969812AT THE BEACHClassical MusicMusicCAD2014-03-225000.02014-02-20 01:00:165501.0successful78CA5019.924983.694529.812014-02-20 01:00:162014-03-2229 days 22:59:44
378652999971898Beach Wrestling DocumentaryDocumentaryFilm & VideoNOK2015-04-2820000.02015-03-29 21:30:3321500.0successful36NO2698.972875.832675.192015-03-29 21:30:332015-04-2829 days 02:29:27
378653999972264IslandaDocumentaryFilm & VideoUSD2012-03-161700.02012-02-15 04:31:1025.0failed1US25.0025.001700.002012-02-15 04:31:102012-03-1629 days 19:28:50
378654999975836Homemade fresh dog food, Cleveland OHSmall BatchFoodUSD2017-04-196500.02017-03-20 22:08:22154.0failed4US0.00154.006500.002017-03-20 22:08:222017-04-1929 days 01:51:38
378655999976312Angela's Poetry (Canceled)PoetryPublishingCAD2014-09-205500.02014-08-06 03:46:070.0canceled0CA0.000.004949.602014-08-06 03:46:072014-09-2044 days 20:13:53
378656999976400ChknTruk Nationwide Charity Drive 2014 (Canceled)DocumentaryFilm & VideoUSD2014-10-1750000.02014-09-17 02:35:3025.0canceled1US25.0025.0050000.002014-09-17 02:35:302014-10-1729 days 21:24:30
378657999977640The TribeNarrative FilmFilm & VideoUSD2011-07-191500.02011-06-22 03:35:14155.0failed5US155.00155.001500.002011-06-22 03:35:142011-07-1926 days 20:24:46
378658999986353Walls of Remedy- New lesbian Romantic Comedy feature unlike any other!!Narrative FilmFilm & VideoUSD2010-08-1615000.02010-07-01 19:40:3020.0failed1US20.0020.0015000.002010-07-01 19:40:302010-08-1645 days 04:19:30
378659999987933BioDefense Education KitTechnologyTechnologyUSD2016-02-1315000.02016-01-13 18:13:53200.0failed6US200.00200.0015000.002016-01-13 18:13:532016-02-1330 days 05:46:07
378660999988282Nou Renmen Ayiti! We Love Haiti!Performance ArtArtUSD2011-08-162000.02011-07-19 09:07:47524.0failed17US524.00524.002000.002011-07-19 09:07:472011-08-1627 days 14:52:13